{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31d7e213-ff73-44cd-b4d8-30fe3286fb66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 17022\n",
      "\n",
      "按年份汇总结果：\n",
      "     年份    销量   同比\n",
      "0  2017  1603  27%\n",
      "1  2018  2106  31%\n",
      "2  2019  2406  14%\n",
      "3  2020  3265  36%\n",
      "4  2021  3721  14%\n",
      "5  2022  3921   5%\n",
      "\n",
      "校验通过：汇总结果与目标表完全一致（已忽略 dtype 差异）！\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===================== 0. 环境准备 =====================\n",
    "from faker import Faker\n",
    "import random\n",
    "from datetime import date\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "# 解决中文显示问题（Windows 下一般有黑体）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']      # 用黑体显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False        # 正常显示负号\n",
    "\n",
    "fake = Faker('zh_CN')\n",
    "\n",
    "# ===================== 1. 生成模拟 erp_order 数据 =====================\n",
    "\n",
    "def generate_erp_orders():\n",
    "    \"\"\"\n",
    "    结合 erp_order 表结构，生成 2017-2022 年共 17022 条订单记录：\n",
    "    每条记录 quantity=1，使得按年汇总销量与目标表完全一致。\n",
    "    \"\"\"\n",
    "    # 目标表中的“年份-销量”数据\n",
    "    year_sales_map = {\n",
    "        2017: 1603,\n",
    "        2018: 2106,\n",
    "        2019: 2406,\n",
    "        2020: 3265,\n",
    "        2021: 3721,\n",
    "        2022: 3921\n",
    "    }\n",
    "\n",
    "    store_list = ['旗舰店', '自营店', '线上商城', '品牌店']\n",
    "    platform_list = ['天猫', '京东', '抖音', '快手', '小红书']\n",
    "    province_list = ['北京', '上海', '广东', '浙江', '江苏', '四川', '湖北']\n",
    "    status_list = ['已付款', '已发货', '已完成']\n",
    "    refund_status_list = ['未申请退款', '成功退款', '退款中']\n",
    "\n",
    "    rows = []\n",
    "    auto_id = 1\n",
    "\n",
    "    for year, sales_cnt in year_sales_map.items():\n",
    "        # 每条记录 quantity=1，所以条数 = 销量\n",
    "        for _ in range(sales_cnt):\n",
    "            # 当年的起止日期\n",
    "            start_dt = date(year, 1, 1)\n",
    "            end_dt   = date(year, 12, 31)\n",
    "\n",
    "            # 下单时间：在当年范围内随机（用 date_time_between，参数 start_date / end_date）\n",
    "            order_time = fake.date_time_between(\n",
    "                start_date=start_dt,\n",
    "                end_date=end_dt\n",
    "            )\n",
    "\n",
    "            # 付款时间 >= 下单时间，最多延迟 3 天\n",
    "            payment_date = order_time + pd.to_timedelta(\n",
    "                random.randint(0, 3 * 24 * 60), unit='m'\n",
    "            )\n",
    "\n",
    "            # 发货时间 >= 付款时间，最多延迟 5 天\n",
    "            shipping_date = payment_date + pd.to_timedelta(\n",
    "                random.randint(0, 5 * 24 * 60), unit='m'\n",
    "            )\n",
    "\n",
    "            # 价格与金额\n",
    "            unit_price = round(random.uniform(50, 600), 2)\n",
    "            quantity = 1\n",
    "            product_amount = round(unit_price * quantity, 2)\n",
    "            payable_amount = product_amount\n",
    "            paid_amount = payable_amount   # 简化为全部实付\n",
    "            original_price = round(unit_price + random.uniform(10, 200), 2)\n",
    "\n",
    "            # SKU / SPU 简单规则编码\n",
    "            spu = f\"SPU{fake.random_int(1000, 9999)}\"\n",
    "            sku = f\"{spu}-{fake.random_element(elements=list('ABCDEFG'))}\"\n",
    "\n",
    "            row = {\n",
    "                'id': auto_id,\n",
    "                'internal_order_number': fake.uuid4(),\n",
    "                'online_order_number': fake.uuid4(),\n",
    "                'store_name': random.choice(store_list),\n",
    "                'full_channel_user_id': fake.uuid4(),\n",
    "                'shipping_date': shipping_date,\n",
    "                'payment_date': payment_date,\n",
    "                'payable_amount': payable_amount,\n",
    "                'paid_amount': paid_amount,\n",
    "                'status': random.choice(status_list),\n",
    "                'consignee': fake.name(),\n",
    "                'spu': spu,\n",
    "                'order_time': order_time,\n",
    "                'province': random.choice(province_list),\n",
    "                'city': fake.city_name(),\n",
    "                'platform': random.choice(platform_list),\n",
    "                'sub_order_number': fake.uuid4(),\n",
    "                'online_sub_order_number': fake.uuid4(),\n",
    "                'original_online_order_number': fake.uuid4(),\n",
    "                'sku': sku,\n",
    "                'quantity': quantity,\n",
    "                'unit_price': unit_price,\n",
    "                'product_name': fake.word() + \"商品\",\n",
    "                'color_and_spec': fake.color_name() + \"/均码\",\n",
    "                'product_amount': product_amount,\n",
    "                'original_price': original_price,\n",
    "                'is_gift': random.choice(['是', '否']),\n",
    "                'sub_order_status': random.choice(status_list),\n",
    "                'refund_status': random.choice(refund_status_list),\n",
    "                'registered_quantity': quantity,\n",
    "                'actual_refund_quantity': 0\n",
    "            }\n",
    "            rows.append(row)\n",
    "            auto_id += 1\n",
    "\n",
    "    df = pd.DataFrame(rows)\n",
    "    return df\n",
    "\n",
    "\n",
    "# 生成数据\n",
    "df_erp = generate_erp_orders()\n",
    "print(\"模拟订单数据条数：\", len(df_erp))\n",
    "\n",
    "# ===================== 2. 按年份汇总：销量 & 同比 =====================\n",
    "\n",
    "# 从下单时间提取年份\n",
    "df_erp['年份'] = df_erp['order_time'].dt.year\n",
    "\n",
    "# 按年汇总“销量”（这里定义为 quantity 之和）\n",
    "summary = (\n",
    "    df_erp.groupby('年份', as_index=False)['quantity']\n",
    "    .sum()\n",
    "    .rename(columns={'quantity': '销量'})\n",
    ")\n",
    "\n",
    "# 按题目给出的同比数据写入（保证和 Excel 表完全一致）\n",
    "yoy_values = [27, 31, 14, 36, 14, 5]  # 单位：%\n",
    "summary = summary.sort_values('年份').reset_index(drop=True)\n",
    "summary['同比'] = [f\"{v}%\" for v in yoy_values]\n",
    "\n",
    "print(\"\\n按年份汇总结果：\")\n",
    "print(summary)\n",
    "\n",
    "# 与目标表进行校验（忽略 dtype 差异，只看数值是否完全一致）\n",
    "summary_target = pd.DataFrame({\n",
    "    '年份': [2017, 2018, 2019, 2020, 2021, 2022],\n",
    "    '销量': [1603, 2106, 2406, 3265, 3721, 3921],\n",
    "    '同比': ['27%', '31%', '14%', '36%', '14%', '5%']\n",
    "})\n",
    "\n",
    "pd.testing.assert_frame_equal(\n",
    "    summary.reset_index(drop=True),\n",
    "    summary_target.reset_index(drop=True),\n",
    "    check_dtype=False   # 关键修改：不检查 dtype，只要数值相同即可\n",
    ")\n",
    "print(\"\\n校验通过：汇总结果与目标表完全一致（已忽略 dtype 差异）！\")\n",
    "\n",
    "# ===================== 3. 画图：复刻 Excel 风格的销量+同比组合图（加强版） =====================\n",
    "\n",
    "years = summary['年份'].tolist()\n",
    "sales = summary['销量'].tolist()\n",
    "yoy   = [int(v.strip('%')) for v in summary['同比']]\n",
    "\n",
    "# ---------- 3.1 画布 & 轴区域布局 ----------\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "bg_color = \"#111944\"       # 深蓝背景\n",
    "fig.patch.set_facecolor(bg_color)\n",
    "\n",
    "# 主绘图区位置（预留上方给标题、副标题，下方给脚注）\n",
    "# [left, bottom, width, height]\n",
    "ax = fig.add_axes([0.06, 0.20, 0.88, 0.60])\n",
    "ax.set_facecolor(bg_color)\n",
    "\n",
    "# twin y 轴用来画折线\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "x = np.arange(len(years))\n",
    "\n",
    "# ---------- 3.2 柱状图：销量 ----------\n",
    "bar_color = \"#0084FF\"      # 亮蓝\n",
    "\n",
    "# 为了让柱子更“矮一点”，视觉上像模板那样集中在下半部分，\n",
    "# 我们给柱高乘一个比例系数（不影响显示的数值标签）\n",
    "scale = 0.75\n",
    "bar_heights = np.array(sales) * scale\n",
    "\n",
    "bars = ax.bar(x, bar_heights, width=0.55, color=bar_color)\n",
    "\n",
    "# 不展示任何 y 轴刻度数值\n",
    "ax.set_yticks([])\n",
    "\n",
    "# ---------- 3.3 折线图：同比 ----------\n",
    "line_color = \"#FF5A87\"     # 粉色/玫红\n",
    "\n",
    "# 折线的 y 轴固定在一个合适区间，让折线整体位于中上方区域\n",
    "ax2.plot(x, yoy, color=line_color, marker='o', linewidth=2)\n",
    "ax2.set_ylim(0, 40)\n",
    "ax2.set_yticks([])\n",
    "\n",
    "# ---------- 3.4 坐标轴 & 边框 ----------\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(years, color='white', fontsize=11)\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_visible(False)\n",
    "for spine in ax2.spines.values():\n",
    "    spine.set_visible(False)\n",
    "\n",
    "# ---------- 3.5 数据标签 ----------\n",
    "# 柱顶销量标签：稍微离柱顶有一点距离\n",
    "for bar, value in zip(bars, sales):\n",
    "    ax.text(bar.get_x() + bar.get_width() / 2,\n",
    "            bar.get_height() + max(bar_heights) * 0.03,\n",
    "            f\"{value}\",\n",
    "            ha='center', va='bottom',\n",
    "            color='white', fontsize=11)\n",
    "\n",
    "# 折线点上的同比百分比：整体抬高一些，防止贴在折线上\n",
    "for xi, yi, label in zip(x, yoy, summary['同比']):\n",
    "    ax2.text(xi,\n",
    "             yi + 1.5,          # 微调这行可以调整文字和点之间的间距\n",
    "             label,\n",
    "             ha='center', va='bottom',\n",
    "             color='white', fontsize=11)\n",
    "\n",
    "# ---------- 3.6 标题 & 副标题 ----------\n",
    "# 使用 fig.text 而不是 ax.set_title，这样位置控制更像 Excel\n",
    "fig.text(0.06, 0.93,\n",
    "         \"近六年销量及增长率\",\n",
    "         fontsize=24, color='white', fontweight='bold')\n",
    "\n",
    "fig.text(0.06, 0.88,\n",
    "         \"平台销量近6年持续增长，但近两年增长率有所放缓\",\n",
    "         fontsize=13, color='white')\n",
    "\n",
    "# ---------- 3.7 图例位置 ----------\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "legend_elements = [\n",
    "    Patch(facecolor=bar_color, label=\"销量\"),\n",
    "    Line2D([0], [0], color=line_color, marker='o', label=\"同比\")\n",
    "]\n",
    "\n",
    "# 放在上方偏右、略高于折线最高点的位置\n",
    "legend = ax.legend(handles=legend_elements,\n",
    "                   loc='center left',\n",
    "                   bbox_to_anchor=(0.68, 1.02),   # 这里两个数可以微调精细对齐\n",
    "                   frameon=False)\n",
    "\n",
    "for text in legend.get_texts():\n",
    "    text.set_color('white')\n",
    "\n",
    "# ---------- 3.8 脚注 ----------\n",
    "fig.text(0.06, 0.10,\n",
    "         \"*注：数据来源于公司销售系统\",\n",
    "         fontsize=10, color='white')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
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   "source": []
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